Applications of time-series analysis to mood fluctuations in bipolar disorder to promote treatment innovation: a case series

Translational Psychiatry
Emily A HolmesM Di Simplicio

Abstract

Treatment innovation for bipolar disorder has been hampered by a lack of techniques to capture a hallmark symptom: ongoing mood instability. Mood swings persist during remission from acute mood episodes and impair daily functioning. The last significant treatment advance remains Lithium (in the 1970s), which aids only the minority of patients. There is no accepted way to establish proof of concept for a new mood-stabilizing treatment. We suggest that combining insights from mood measurement with applied mathematics may provide a step change: repeated daily mood measurement (depression) over a short time frame (1 month) can create individual bipolar mood instability profiles. A time-series approach allows comparison of mood instability pre- and post-treatment. We test a new imagery-focused cognitive therapy treatment approach (MAPP; Mood Action Psychology Programme) targeting a driver of mood instability, and apply these measurement methods in a non-concurrent multiple baseline design case series of 14 patients with bipolar disorder. Weekly mood monitoring and treatment target data improved for the whole sample combined. Time-series analyses of daily mood data, sampled remotely (mobile phone/Internet) for 28 days pre- and post-t...Continue Reading

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Citations

Dec 21, 2016·Bipolar Disorders·Martina Di SimplicioEmily A Holmes
Jan 29, 2019·CNS Spectrums·Julie L JiMartina Di Simplicio
Mar 20, 2020·PloS One·Alba Pedro-ZapaterClemente Rodríguez
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Jul 1, 2020·Behavioural and Cognitive Psychotherapy·Lalitha IyaduraiEmily A Holmes
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Sep 13, 2020·Journal of Affective Disorders·Caitlin O'DonnellStephanie Burnett Heyes

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Bipolar disorder is characterized by manic and/or depressive episodes and associated with uncommon shifts in mood, activity levels, and energy. Discover the latest research this illness here.